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Data Visualization with Python and Matplotlib Training Course

2k learners

In this course, you will learn how to present data in an explicit manner using the Python as a Data Visualization Tool. This course will help you learn how to arrange critical and meaningful data in a simple yet sophisticated way that is required for decision making.

  • Access to GreyCampus platform

  • GreyCampus Course completion certificate

Subscribe to this course + 29 courses
USD 50

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Program Overview

Data visualization means representing the data in a visual format. Visual formats include graphs, charts, and pictograms. It's of great advantage to learn to deploy data visualization through Python using Matplotlib. Matplotlib is a library of Python that helps in the viewing of the data. You will learn how to deploy various commands for creating multiple graphs, 3D scatter diagrams, and Pie Charts among others. In this course, you will also learn about actual geographical plotting using Matplotlib extension called Basemap.
  • 1-year access to audio-video lectures
  • Course completion certificate

Program Outline


This course helps you visualize varied forms of 2D and 3D graphs, like bar charts, line graphs, scatter plots, etc. You will also learn how to customize those graphs such as modifying lines, colors, etc.


This course is aligned to help you learn various ways to visually present python data


On finishing this course, you will gain a complete understanding of the options available for visualizing data and know how to create well presented, visually appealing graphs.

  • Course Introduction
    Getting Matplotlib And Setting Up
  • Different types of basic Matplotlib charts
    Section Introduction
    Basic matplotlib graph
    Labels, titles and window buttons
    Bar Charts
    Scatter Plots
    Stack Plots
    Pie Chart
    Loading data from a CSV
    Loading data with NumPy
    Section Conclusion
  • Basic Customization Options
    Section Introduction
    Source for our Data*
    Parsing stock prices from the internet*
    Plotting basic stock data*
    Modifying labels and adding a grid*
    Converting from unix time and adjusting subplots*
    Customizing ticks*
    Fills and Alpha*
    Add, remove, and customize spines*
    Candlestick OHLC charts*
    Styles with Matplotlib*
    Creating our own Style*
    Live Graphs*
    Adding and placing text*
    Annotating a specific plot*
    Dynamic annotation of last price*
    Section Conclusion
  • Advanced Customization Options
    Section Introduction
    Basic suplot additions*
    Subplot2grid *
    Incorporating changes to candlestick graph*
    Creating moving averages with our data*
    Adding a High minus Low indicator to graph*
    Customizing the dates that show*
    Label and Tick customizations*
    Share X axis*
    Multi Y axis*
    Customizing Legends*
    Section Conclusion
    • Geographical Plotting with Basemap
      Section Introduction
      Downloading and installing Basemap
      Basic basemap example
      Customizing the projection
      More customization, like colors, fills, and forms of boundaries
      Plotting Coordinates*
      Connecting Coordinates*
      Section Conclusion
    • 3D graphing
      Section Introduction
      Basic 3D graph example using wire_frame
      3D scatter plots
      3D Bar Charts
      More advanced Wireframe example
      Section Conclusion
    • Course Conclusion

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